[Opening Scene]: Billy Dean is pacing the office. He’s struggling to keep his delivery trucks at full capacity and on the road. Random breakdowns, unexpected employee absences, and unscheduled truck maintenance are impacting bookings, revenues and ultimately customer satisfaction. He keeps hearing from his business customers how they are leveraging data science to improve their business operations. Billy Dean starts to wonder if data science can help him. As he contemplates what data science can do for him, he slowly drifts off to sleep, and visions of Data Science starts dancing in his head…

Data science is about identifying those variables and metrics that might be better predictors of performance

The key to a successful analytical model is having a robust set of variables against which to test for their predictive capabilities. And the key to having a robust set of variables from which to test is to get the business users engaged early in the process.

[A confused Billy Dean]: Okay, but I’m still confused. I mean, how does this really apply to my business?

[A patient Wizard Wei]: Well, let’s say that you are trying to predict which of your routes are likely to have under-capacity loads so that you can combine loads. In order to identify those variables that might be better predictors of under-capacity routes, you might ask your business users:

What data might you want to have in order to predict under-capacity routes?

The business users are likely to come up with a wide variety of variables, including:

Customer name

Ship to location

Customer industry

Building permits

Customer tenure

Change in customer size

Customer stock price

Customer D&B rating

Types of products hauled

Time of year

Seasonality/Holidays

Day of week

Traffic

Weather

Local Events

Distance from distribution center

Open headcount on Indeed.com

Tenure of logistics manager

The Data Science team will then gather these variables, perform some data transformations and enrichment, and then look for variables and combinations of variables that yield the best predictive results regarding under-capacity routes (see Figure 1).

Figure 1: Data Science Process

Role of Artificial Intelligence[A less confuse Billy Dean]: Ah, I think I understand, but what about all this talk about artificial intelligence? From some of these commercials on TV, it appears that robots with artificial intelligence will be ruling the world. Can you say Skynet?

[A still patient Wizard Wei]: Ah, that’s just marketing. Artificial intelligence is just one of many different tools in the predictive analytics kit bag of a data scientist. But artificial intelligence – while embracing some very sophisticated mathematical, data enrichment and computing techniques – is really pretty straightforward. All artificial intelligence is trying to do is to find and quantify relationships between variables buried in large data sets (see Figure 2).

Figure 2: Understanding Artificial Intelligence

[An inquisitive Billy Dean]: Okay, I’m starting to get it, but there seems to be some many different analytic and predictive algorithms from which to choose. How does the business user know where to start?

[A growing frustrated Wizard Wei]: Ah, that’s the secret to the process. Business users don’t need to know which algorithms to use; they need to be able to identify those variables that might be better predictors of performance. It is up to the data science team to determine which variables are the most appropriate by testing the different algorithms.

Data Mining, Machine Learning and Artificial Intelligence (including areas such as cognitive computing, statistics, neural networks, text analytics, video analytics, etc.) are all members of the broader category of data science tools. Our data scientist team has experts in each of these areas, though no one data scientist is an expert at all of them (in spite of what they tell me). The different data science tools are used in different scenarios for different needs. Think of one of your mechanics. They have a large toolbox full of different tools. They determine what tools to use to fix a truck based upon the problem they are trying to solve. That’s exactly what a data scientist is doing, just with a different toolbox of algorithms.

No single algorithm is best over whole domain; so different algorithms are needed to cover different domains. Often combinations of algorithms are used in order to achieve the best results. To be honest, it’s like a giant jigsaw puzzle with the data science team constantly testing different combinations of metrics, data enrichment and algorithms until they find the combination that yields the best results.

[An enlightened Billy Dean]: I think I’ve finally got it. All of these different algorithms and techniques are just trying to help predict what is likely to happen so that I can make better operational and customer issues. But what’s the realm of what’s possible with data and analytics; I mean, how effective can my organization become at leveraging data and analytics to power my business?

[A proud Wizard Wei]: Great question, and the heart of the big data and data science conversation. Figure 3 shows how you could use these different data science tools to progress up the Big Data Business Model Maturity Index; to transition from running your business on Descriptive analytics that tell you what happened (Monitoring stage) to Predictive analytics that tell you what is likely to happen (Insights stage) to Prescriptive analytics that tell you what they should do (Optimization stage).

Figure 3: Leveraging Artificial Intelligence to drive Business Value

In the end, the data and the analytics are only useful if they help you optimize key operational processes, reduce compliance and security risks, uncover new revenue opportunities and create a more compelling, more prescriptive customer engagement. In the end, data and analytics are all about your business.

As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.